Application of FT-IR spectroscopy and chemometric technique for the identification of three different parts of Camellia nitidissima and discrimination of its authenticated product.

Camellia nitidissima C.W. Chi is a golden camellia recognized in Chinese herbology and widely used as tea and essential oil in Chinese communities. Due to its diverse pharmacological properties, it can be used to treat various diseases. However, unethical sellers adulterated the flower with other parts of Camellia nitidissima in their product. This study used an integrated tri-step infrared spectroscopy method and a chemometric approach to distinguish C. nitidissima's flowers, leaves, and seeds. The three different parts of C. nitidissima were well distinguished using Fourier transform infrared spectroscopy (FT-IR), second-derivative infrared (SD-IR) spectra, and two-dimensional correlation infrared (2D-IR) spectra. The FT-IR and SD-IR spectra of the samples were subjected to principal component analysis (PCA), PCA-class, and orthogonal partial least square discriminant analysis (OPLS-DA) for classification and discrimination studies. The three parts of C. nitidissima were well separated and discriminated by PCA and OPLS-DA. The PCA-class model's sensitivity, accuracy, and specificity were all >94%, indicating that PCA-class is the good model. In addition, the RMSEE, RMSEP, and RMSECV values for the OPLS-DA model were low, and the model's sensitivity, accuracy, and specificity were all 100%, showing that it is the excellent one. In addition, PCA-class and OPLS-DA obtained scores of 27/32 and 26/32, respectively, for detecting adulterated and other TCM reference flower samples from C. nitidissima. Combining an infrared spectroscopic method with a chemometric approach proved that it is possible to differentiate distinct sections of C. nitidissima and discriminate adulterated samples of C.nitidissima flower.

Utilizing 3D Slicer to incorporate tomographic images into GATE Monte Carlo simulation for personalized dosimetry in yttrium-90 radioembolization.

PURPOSE: Monte Carlo (MC) simulation is an important technique that can help design advanced and challenging experimental setups. GATE (Geant4 application for tomographic emission) is a useful simulation toolkit for applications in nuclear medicine. Transarterial radioembolization is a treatment for liver cancer, where microspheres embedded with yttrium-90 (90 Y) are administered intra-arterially to the tumor. Personalized dosimetry for this treatment may provide higher dosimetry accuracy compared to the conventional partition model (PM) calculation. However, incorporation of three-dimensional tomographic input data into MC simulation is an intricate process. In this article, 3D Slicer, free and open-source software, was utilized for the incorporation of patient tomographic images into GATE to demonstrate the feasibility of personalized dosimetry in hepatic radioembolization with 90 Y. METHODS: In this article, the steps involved in importing, segmenting, and registering tomographic images using 3D Slicer were thoroughly described, before importing them into GATE for MC simulation. The absorbed doses estimated using GATE were then compared with that of PM. SlicerRT, a 3D Slicer extension, was then used to visualize the isodose from the MC simulation. RESULTS: A workflow diagram consisting of all the steps taken in the utilization of 3D Slicer for personalized dosimetry in 90 Y radioembolization has been presented in this article. In comparison to the MC simulation, the absorbed doses to the tumor and normal liver were overestimated by PM by 105.55% and 20.23%, respectively, whereas for lungs, the absorbed dose estimated by PM was underestimated by 25.32%. These values were supported by the isodose distribution obtained via SlicerRT, suggesting the presence of beta particles outside the volumes of interest. These findings demonstrate the importance of personalized dosimetry for a more accurate absorbed dose estimation compared to PM. CONCLUSION: The methodology provided in this study can assist users (especially students or researchers who are new to MC simulation) in navigating intricate steps required in the importation of tomographic data for MC simulation. These steps can also be utilized for other radiation therapy related applications, not necessarily limited to internal dosimetry.

A novel machine learning scheme for classification of medicinal herbs based on 2D-FTIR fingerprints.

A proof-of-concept medicinal herbs identification scheme using machine learning classifiers is proposed in the form of an automated computational package. The scheme makes use of two-dimensional correlation Fourier Transformed Infrared (FTIR) fingerprinting maps derived from the FTIR of raw herb spectra as digital input. The prototype package admits a collection of 11 machine learning classifiers to form a voting pool. A common set of oversampled dataset containing 5 different herbal classes is used to train the pool of classifiers on a one-verses-others manner. The collections of trained models, dubbed the voting classifiers, are deployed in a collective manner to cast their votes to support or against a given inference fingerprint whether it belongs to a particular class. By collecting the votes casted by all voting classifiers, a logically designed scoring system will select out the most probable guess of the identity of the inference fingerprint. The same scoring system is also capable of discriminating an inference fingerprint that does not belong to any of the classes the voting classifiers are trained for as the 'others' type. The proposed classification scheme is stress-tested to evaluate its performance and expected consistency. Our experimental runs show that, by and large, a satisfactory performance of the classification scheme of up to 90 % accuracy is achieved, providing a proof-of-concept viability that the proposed scheme is a feasible, practical, and convenient tool for herbal classification. The scheme is implemented in the form of a packaged Python code, dubbed the "Collective Voting" (CV) package, which is easily scalable, maintained and used in practice.

Effects of oxygen variation on the improved structural stability, electronic and optical properties of ZnTeO compounds.

Crystalline ZnTeO thin films are promising materials for next generation photovoltaics. However, their structural stability and optical nonlinearity potential in bulk form have not been reported. Here, structural, electronic and optical properties of ZnTeO composites have been thoroughly studied using genetic algorithm and density functional theory (DFT). Energetically, mechanically and dynamically stable O-rich phases, namely Zn2Te2O6 and ZnTeO4, were obtained. Ground-state properties such as lattice constants and simulated XRD were analyzed and compared to the experimental literature wherever possible. With a G 0 W 0 corrected band gap, these semiconducting phases display several desirable features, namely, Jahn-Teller distorted cations, hardness and shear anisotropy-induced optical nonlinearity that increase monotonically as O concentration elevates. Such trends appear to be consistent with that seen in the experimental study of ZnTeO thin film. It is observed that Zn-d, Te-p  and O-p  states have immense influence towards the electronic properties of these structures. Both phases exhibit steep elevation of absorption throughout the ultraviolet (UV) range, hitting peak value of ~5.0 × 105 cm-1. Of particular interest, the non-centrosymmetric ZnTeO4 has second harmonic generation coefficients (9.84 pm V-1 and 2.33 pm V-1 at static limit) greater than borates crystal and large birefringence that exceeds 0.08 in deep UV region, thus highlighting its potential pedigree as new optical materials in UV range.

First-principles studies on the effects of halogen adsorption on monolayer antimony.

Using first-principles calculations, we carry out systematic studies on the electronic, magnetic and structural properties of halogenated ß-phase antimonene. We consider two different levels of halogen adatom coverage i.e. Θ = 1/8 and Θ = 1/18. It is found that F, Cl and Br adatoms act as acceptors whereas the I adatom acts as a donor. For a high coverage of Θ = 1/8, halogenated ß-phase antimonene exhibits metallic characteristics. With a lower coverage of Θ = 1/18, through the adsorption of F, Cl and Br the semiconducting unstrained antimonene becomes metallic. In contrast, I-adsorbed antimonene remains semiconducting but exhibits magnetic behavior. We further investigate the effects of bi-axial strain on the halogenated ß-phase antimonene. It is found that bi-axial strain can only induce ferromagnetism on the halogenated antimonene at Θ = 1/18. However, the ferromagnetism is suppressed when the applied strain is high. We uncover that the emergence of strain-dependent magnetism is attributed to the presence of localized states in the bandgap resulting from collective effects of bi-axial strain and the adsorption of halogen atoms.

Thermoelectric and piezoelectric properties of the predicted AlxIn1-xN composites based on ab initio calculations.

Theoretical investigations of the thermoelectric and piezoelectric characteristics in the AlxIn1-xN system have been carried out based on a first principles approach in combination with the semi-classical Boltzmann transport concept and density functional perturbation theory. Based on our previous work, herein, the study specimens Al5InN6, Al6In2N8, Al4In2N6, Al3In3N6, Al2In4N6, and AlIn7N8 have been predicted to be stable phases. These novel phases intrinsically exhibit moderate positive Seebeck curves (199.1-284.6 µV K-1) and a ZT close to unity that varies marginally over a broad temperature range of 200-800 K, demonstrating the sign of good bipolar effect tolerance. Addition of heftier elements, such as In, results in lower thermal conductivity, which in turn generates a high power factor (0.019-0.345 W m-1 K-2) in these alloys. While hole doping enhances the peak Seebeck coefficient of each phase, the electrical conductivity has been greatly compromised, resulting in a lower power factor. These composites also exhibit large piezoelectric constants, in which their respective largest piezoelectric tensor is several orders higher than that of quartz. The decomposition process shows that In and N are the main contributors of the internal piezoelectric term. Overall results indicate that AlxIn1-xN show bright prospects in thermoelectric and piezoelectric applications.

Investigation of Melting Dynamics of Hafnium Clusters.

Melting dynamics of hafnium clusters are investigated using a novel approach based on the idea of the chemical similarity index. Ground state configurations of small hafnium clusters are first derived using Basin-Hopping and Genetic Algorithm in the parallel tempering mode, employing the COMB potential in the energy calculator. These assumed ground state structures are verified by using the Low Lying Structures (LLS) method. The melting process is carried out either by using the direct heating method or prolonged simulated annealing. The melting point is identified by a caloric curve. However, it is found that the global similarity index is much more superior in locating premelting and total melting points of hafnium clusters.